Sasen Cain, University of California San Diego, United States; Matthew Cain, United States Army and Tufts University, United States

Abstract:

How do ensemble representations subvert well-established perceptual capacity limits, such as attention and visual working memory? Here we ask if an off-the-shelf texture statistics representation can explain ensemble judgments, without explicitly representing and measuring objects. We found that an ideal observer using only texture statistics was able to perform an ensemble mean size comparison task as well as humans, and further, that this model replicated previously unexplained human perceptual biases. This means that the magic of ensemble representations could actually be due to the compressive power of texture statistics. We thus present the first generalizable computational account of ensemble perception, while also explaining a long-standing mystery about limitations on human performance in ensemble tasks.